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This is an introduction to Bayesian statistics and decision theory, including advanced topics such as Monte Carlo methods. This new edition contains several revised chapters and a new chapter on model choice.

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Editorial Reviews

Review

From the reviews of the second edition:

SHORT BOOK REVIEWS

"The text reads fluently and beautifully throughout, with light, good-humoured touches that warm the reader without being intrusive. There are many examples and exercises, some of which draw out the essence of work of other authors. Each chapter ends with a "Notes" section containing further brief descriptions of research papers. A reference section lists about eight hundred and sixty references. Each chapter begins with a quotation from "The Wheel of Time" a sequence of books by Robert Jordan. Only a few displays and equations have numbers attached. This is an extremely fine, exceptional text of the highest quality."ISI Short Book Reviews, April 2002

JOURNAL OF MATHEMATICAL PSYCHOLOGY

"This book is an excellent introduction to Bayesian statistics and decision making. The author does an outstanding job in explicating the Bayesian research program and in discussing how Bayesian statistics differs form fiducial inference and from the Newman-Pearson likelihood approach…The book would be well suited for a graduate-level course in a mathematical statistics department. There are numerous examples and exercises to enhance a deeper understanding of the material. The writing is authoritative, comprehensive, and scholarly."

"This book is a publication in the well-known Springer Series in statistics published in 2001. It is a textbook that presents an introduction to Bayesian statistics and decision theory for graduate level course … . The textbook contains a wealth of references to the literature; therefore it can also be recommended as an important reference book for statistical researchers. … for those who want to make a Bayesian choice, I recommend that you make your choice by getting hold of Robert’s book, The Bayesian Choice." (Jan du Plessis, Newsletter of the South African Statistical Association, June, 2003)

"This is the second edition of the author’s graduate level textbook ‘The Bayesian choice: a decision-theoretic motivation.’ … The present book is a revised edition. It includes important advances that have taken place since then. Different from the previous edition is the decreased emphasis on decision-theoretic principles. Nevertheless, the connection between Bayesian Statistics and Decision Theory is developed. Moreover, the author emphasizes the increasing importance of computational techniques." (Krzysztof Piasecki, Zentralblatt MATH, Vol. 980, 2002)

Language Notes

Text: English (translation) Original Language: French
--This text refers to the
Hardcover
edition.

More About the Author

Christian P. Robert is Professor of Statistics at Université Paris-Dauphine since 2000, as well as the head of the Stat Lab at CREST, INSEE, Paris. He became a senior member of the Institut Universitaire de France in OCtober 2010. He was co-editor of the Journal of the Royal Statistical Society, Series B, from 2006 till 2010. He was the president of the International Society for Bayesian Analysis (ISBA) in 2008. His latest book is Introducing Monte Carlo Methods with R translated into French in 2011 (and soon to be translated into Japanese). He is currently working on the new editions of Bayesian Core and Monte Carlo Statistical Methods.

Most Helpful Customer Reviews

I feel like the intended audience of this book is more the authors peers than it is graduate students: the author assumes you have a solid theoretical foundation in bayesian statistics. Much of the discourse is about the reasons for choosing a bayesian framework instead of a frequentist framework. For those who are already familiar with the theoretical underpinnings, this book likely serves as a great argument for bayesian statistics and is a nice unifying framework for the key concepts. It is clear that the author is passionate about the topic, very knowledgeable about the material and very precise in presenting the material. His arguments about the bayesian choice are well-reasoned and well-balanced.

However, for those who want to apply bayesian statistics to a problem in their own research area, there are likely better books. The author uses many concepts before introducing them. In many cases, the introduction of a concept is so brief as to only serve as a reminder for those who already know the topic well. I have taken several graduate courses in statistics and I have studied most of the topics listed in the table of contents, yet I find this book difficult to follow.

I feel reviews are often colored by the (often unknown) background of the reviewer, so I'm including a little of my background: I have a phd in computer science and my thesis topic was computer vision. I am now working on machine learning problems and when I bought this book I felt a stronger background in bayesian statistics would help me.

Robert is the author or co-author of a number of excellently written statistical texts from a Bayesian viewpoint. This text is no exception. It was quite popular in its first edition in 1994 (a translation and correction of an earlier text in French). The rapid advancement in Bayesian applications and theory due to the success of computer-intensive methods such as Markov Chain Monte Carlo Methods justifies an update in 2001.Chapter 7 on model choice is entirely new and Chapter 6 on Bayesian calculations is extensively revised. Chapter 10 on hierarchical models and empirical Bayes extensions has been supplemented with a number of recent examples. Bayesian hierarchical models are now being used in the development of clinical trials particularly in the medical device industry.

This is an advanced graduate text in Bayesian statistics and has a wealth of references to the literature. In that respect it is very similar to the fine text by Bernardo and Smith (1994) "Bayesian Theory" but is a little more current.

An important reference for all statistical researchers, I highly recommend it for a graduate course text in Bayesian methods as well as for a reference book.

The book is a good introduction to bayesian decision theory. The plenty examples in the book are helpful in the understanding of the subject, but one could wish a more detailed description of the bayesian paradigm. People with little experience with statistics should maybe consider another book.

This is a very well-respected book. Its status as the winner of the 2004 DeGroot Prize is evidence of its excellence. If you want to develop a strong background in Bayesian statistics, then this is the book you want. This book takes a much more rigorous approach to Bayesian statistics than Bayesian Data Analysis. Robert develops both the decision theoretic background of Bayesian statistics up to the level of The Theory of Point Estimation by Lehmann and MCMC computation including practical implementation issues. The author is to be commended for writing a book that contains very advanced material from mathematical statistics, but the book can be used by a wide audience since the parts on Bayesian computation are easily accessible. If you want to become serious about Bayesian statistics, then this will be a very useful book to you. Another book that may be of interest is Monte Carlo Methods by Robert and Casella.